Anomaly Detection in Software System Logs Using Machine Learning

Abstract

Modern software systems generate large volumes of logs that record events, states, errors, and user or component activities. Manual inspection is increasingly unable to support timely fault diagnosis, performance monitoring, and security analysis. This review examines how machine learning is used to detect anomalies in software system logs, with emphasis on the full pipeline from raw log messages to operational alerts. It first explains software logs, event templates, anomaly types, labels, and commonly used evaluation measures. It then compares log parsing, window construction, count-based features, semantic embeddings, classical machine learning, deep sequence models, self-supervised learning, and parser-free approaches. Recent journal studies show that model accuracy depends not only on architecture but also on parsing distinguishability, feature representation, window design, label quality, dataset imbalance, and cross-system differences. Traditional models can remain competitive when features and tuning are appropriate, while transformer, contrastive, and end-to-end methods improve semantic modeling and adaptation to unseen events. However, data drift, limited labels, inconsistent benchmarks, false alarms, interpretability, privacy, and deployment cost remain important barriers. Future work should prioritize adaptive, transferable, explainable, and privacy-aware systems that combine accurate detection with practical maintenance needs.